An experimental study on minimum arc current of relay contacts and possible re-interpretation of the meaning thereof

Hasegawa, Makoto; Tamaki, Y.; Kamada, Yoshinobu

doi:10.1109/holm.2006.284080

Cited by 8 publications

(2 citation statements)

References 7 publications

(6 reference statements)

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“…Prior to 1.6 ms, one can observe a sharp decrease in current turning the energy balance of the arc negative i.e, the arc looses more energy than supplied through the cathode. Hence, it becomes unstable, the current drops below 1 A, and the voltage reaches 30 V. Subsequently the arc is extinct at 0.5 A which agrees with the measurements reported by [137].…”

Section: Appendix D Contact Breakingsupporting

confidence: 90%

Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful Life emphasises the limited analysis and understanding of expressive EMR degradation indicators, as well as accessibility and use of EMR life cycle data sets. Prioritising these open challenges, a deep learning pipeline is presented in a prognostic context termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a remaining useful switching actuations forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact-Voltage, Contact-Current). Monte-Carlo Dropout is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting mean absolute percentage error of ±12 % over the course of the entire EMR life. INDEX TERMSElectromagnetic relay, prognostics, prognostics and health management, predictive maintenance, remaining useful life, artificial intelligence, deep learning, temporal convolutional networks, Monte-Carlo dropout. ABBREVIATIONS AT Arcing time. BT Bounce time. CAE Convolutional auto encoder. CC Coil current. CI Contact current. CNN Convolutional neural network. CR Contact resistance. CT Closing time. CV Contact voltage. DCR Dynamic contact resistance. DI Degradation indicator. EI Exponential indexing. The associate editor coordinating the review of this manuscript and approving it for publication was Sajid Ali . EMR Electromagnetic relay. EMRUA Electromagnetic relay useful actuation pipeline. EOL End of life. FC Fully connected layer. GI Growing-sequence indexing. LI Linear indexing. LSTM Long-short-term-memory network. MAE Mean absolute error. MAPE Mean absolute percentage error. MCD Monte-carlo dropout. MVTD Multivariate time series data. NN Neural network. OT Over-travel time.

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Section: Appendix D Contact Breakingsupporting

confidence: 90%

Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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“…The author became interested in the minimum arc discharge characteristics of contact materials, and reported that the conventional minimum arc current value should be considered, not as the lower limit of arcing, but rather as a possible threshold current level at which arc discharges on the order of at least several tens of [ts will occur at occurrence rate close to 100% [1][2][3][4]. The author also confirmed the previous report of Ben Jemaa et al [5][6][7][8] in which when drawing the characteristics between the average arc duration values (in the logarithmic scale) and the load current, slopes of the plots tended to change at around the conventional minimum arc current value for the contact materials tested.…”

Section: Introductionmentioning

confidence: 99%

An experimental study of the minimum arc current of silver contacts with different opening speeds

Hasegawa

2007

Electrical Contacts - 2007 Proceedings of the 53rd IEEE Holm Conference on Electrical Contacts

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In order to study the influence of contact opening speed on occurrence rates of break arcs, Ag contacts were operated to break inductive DC load currents from 0.1 A to 0.6 A at 14 V with contact opening speeds of 0.5 mm/s to 10 mm/s in a switching mechanism employing a stepping motor. Occurrence rates of break arcs and their average durations were determined at each current level under the respective contact opening speeds. From the results, irrespective of the opening speed values, the arc occurrence rate was almost 100% at 0.4 A. For all of the contact opening speeds tested, the conventional minimum arc current level can be considered, not as the lower limit for arc ignition, but rather as the threshold level for arc discharges of certain duration to occur at the rate of almost 100%. Moreover, in the current range from 0.2 to 0.4A, slight differences in the arc occurrence rates were seen with differences in the contact opening speeds. The average arc durations were around 50 ps at the conventional minimum arc current level of 0.4 A, and not influenced significantly by changes in the tested range of contact opening speeds.

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